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https://github.com/pytorch/pytorch.git
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b11593c31b
This is follow-up of #164653 to continue applying `UP035` fixes. The purpose is to finally enable this rule. Pull Request resolved: https://github.com/pytorch/pytorch/pull/165214 Approved by: https://github.com/ezyang
536 lines
17 KiB
Python
536 lines
17 KiB
Python
# mypy: allow-untyped-defs
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# Copyright (c) Meta Platforms, Inc. and affiliates
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import logging
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import operator
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from collections.abc import Sequence
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from typing import Any, Optional
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import torch
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from torch.fx.node import map_aggregate
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from torch.nn.attention.flex_attention import BlockMask
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from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
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__all__ = [
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"TensorChunkSpec",
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"split_args_kwargs_into_chunks",
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"merge_chunks",
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]
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logger = logging.getLogger(__name__)
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"""
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_debug_mask_minibatches specifies to send masked versions of the mini-batch
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through instead of micro-batch slices--this can be used for more stable
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numerical testing (see [A Note About Correctness Testing])
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"""
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_debug_mask_minibatches = False
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class _CustomReducer:
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"""
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Custom reducer class that can be used to specify a custom operation that
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reduces losses of multiple microbatches into one value.
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Example:
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>>> # xdoctest: +SKIP
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>>> sum_reducer = _CustomReducer(
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>>> torch.tensor(0.0),
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>>> lambda a, b: a + b
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>>> )
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"""
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def __init__(self, init_value, reduce_fn):
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self.init_value = init_value
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self.reduce_fn = reduce_fn
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class _LossReducer(_CustomReducer):
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pass
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sum_reducer = _LossReducer(torch.tensor(0.0), operator.add)
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# Default chunking dimension is 0. This is used for the case where the user did
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# not specify a chunking dimension.
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DEFAULT_CHUNK_DIM = 0
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class TensorChunkSpec:
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"""
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Class used to specify chunking of inputs
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"""
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def __init__(self, split_dim):
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self.split_dim = split_dim
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split_dim: int
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def __repr__(self):
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return (
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f"{self.__class__.__module__}.{self.__class__.__name__}({self.split_dim})"
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)
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def __str__(self):
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return f"TensorChunkSpec({self.split_dim})"
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@staticmethod
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def from_tuple(
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chunk_dims: tuple[int, ...],
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):
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"""
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A helper for creating a tuple of `TensorChunkSpec` from a tuple of chunk
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dimensions (int's).
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Example:
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>>> # xdoctest: +SKIP
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>>> # There are three positional arguments to the model, and
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>>> # we are chunking them along dimension 0, 0 and 1, respectively
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>>> args_chunk_spec = TensorChunkSpec.from_tuple((0, 0, 1))
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"""
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args_chunk_spec = map_aggregate(
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chunk_dims,
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lambda dim: TensorChunkSpec(dim), # type: ignore[arg-type,return-value]
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)
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return args_chunk_spec
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@staticmethod
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def from_dict(
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chunk_dims: dict[str, int],
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):
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"""
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A helper for creating a dictionary of `TensorChunkSpec` from a
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dictionary of chunk dimensions (int's).
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Example:
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>>> # xdoctest: +SKIP
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>>> # Chunk dimension 0 for the "id" argument, 1 for the "mask" argument
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>>> kwargs_chunk_spec = TensorChunkSpec.from_dict({"id": 0, "mask": 1})
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"""
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kwargs_chunk_spec = map_aggregate(
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chunk_dims,
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lambda dim: TensorChunkSpec(dim), # type: ignore[arg-type,return-value]
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)
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return kwargs_chunk_spec
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# Class used to specify replication of inputs
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class _Replicate:
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pass
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def _split_block_mask(
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block_mask: BlockMask,
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num_chunks: int,
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) -> list[BlockMask]:
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"""Given a block mask, split the block mask along the batch dimension (dim0).
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Args:
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block_mask: Block mask to split
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num_chunks: Number of chunks to split the block mask into
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Returns:
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chunk_block_masks: List of chunked block masks
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"""
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# BlockMask will broadcast if B is 1.
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if block_mask.kv_num_blocks.size(0) == 1:
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return [block_mask] * num_chunks
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assert block_mask.kv_num_blocks.size(0) >= num_chunks, (
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"Block mask has fewer batch size than the number of chunks. "
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)
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batch_dim = 0
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kv_num_blocks_chunks = torch.tensor_split(
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block_mask.kv_num_blocks, num_chunks, batch_dim
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)
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kv_indices_chunks = torch.tensor_split(block_mask.kv_indices, num_chunks, batch_dim)
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full_kv_num_blocks_chunks = (
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torch.tensor_split(block_mask.full_kv_num_blocks, num_chunks, batch_dim)
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if block_mask.full_kv_num_blocks is not None
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else [None] * num_chunks
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)
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full_kv_indices_chunks = (
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torch.tensor_split(block_mask.full_kv_indices, num_chunks, batch_dim)
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if block_mask.full_kv_indices is not None
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else [None] * num_chunks
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)
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chunk_block_masks = []
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batch_offset = 0
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for chunk_idx in range(num_chunks):
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def create_mask_mod(idx):
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def batch_offset_mask_mod(b, h, q_idx, kv_idx):
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b_offset = torch.full_like(b, idx)
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return block_mask.mask_mod(b + b_offset, h, q_idx, kv_idx)
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return batch_offset_mask_mod
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chunk_block_masks.append(
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BlockMask.from_kv_blocks(
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kv_num_blocks=kv_num_blocks_chunks[chunk_idx],
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kv_indices=kv_indices_chunks[chunk_idx],
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full_kv_num_blocks=full_kv_num_blocks_chunks[chunk_idx],
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full_kv_indices=full_kv_indices_chunks[chunk_idx],
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BLOCK_SIZE=block_mask.BLOCK_SIZE,
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mask_mod=create_mask_mod(batch_offset),
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seq_lengths=block_mask.seq_lengths,
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)
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)
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batch_offset += kv_num_blocks_chunks[chunk_idx].size(0)
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return chunk_block_masks
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def _split_tensor(
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tensor: torch.Tensor,
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spec: TensorChunkSpec,
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num_chunks: int,
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) -> Sequence[torch.Tensor]:
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"""Given a tensor, and a chunking spec, split the tensor.
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Args:
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tensor: Tensor to split
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spec: Chunking spec
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num_chunks: Number of chunks to split the tensor into
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Returns:
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chunk_tensors: List of chunked tensors
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"""
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assert tensor.size(spec.split_dim) >= num_chunks, (
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f"Tensor size {tensor.size(spec.split_dim)} is smaller than num_chunks"
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)
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chunk_tensors = torch.tensor_split(tensor, num_chunks, spec.split_dim)
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if not _debug_mask_minibatches:
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return chunk_tensors
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expanded_chunks = []
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split_dim_idx = 0
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for chunk_tensor in chunk_tensors:
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new_val = torch.zeros_like(tensor)
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upper_idx = split_dim_idx + chunk_tensor.size(spec.split_dim)
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slice_indices = [slice(None, None, None)] * new_val.ndim
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slice_indices[spec.split_dim] = slice(split_dim_idx, upper_idx)
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new_val[slice_indices] = chunk_tensor
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expanded_chunks.append(new_val)
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split_dim_idx += chunk_tensor.size(spec.split_dim)
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return expanded_chunks
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def _shard_dict_of_args(
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args_dict,
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args_chunk_spec,
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num_chunks,
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):
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"""
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Given a dictionary of args, and a dictionary of chunking specs, shard the
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args according to the chunking specs.
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Args:
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args_dict: Dictionary of args
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args_chunk_spec: Dictionary of chunking specs
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num_chunks: Number of chunks to shard the args into
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Returns:
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args_split: List of sharded args
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"""
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if not args_dict:
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return [{} for _ in range(num_chunks)]
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assert len(args_dict) == len(args_chunk_spec), (
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f"args_dict.keys() = {list(args_dict.keys())} "
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f"args_chunk_spec.keys() = {list(args_chunk_spec.keys())}"
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)
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assert args_chunk_spec is not None # Should have been set by caller
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values, tree_spec = tree_flatten(args_dict)
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chunk_specs, _ = tree_flatten(args_chunk_spec)
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# First check and find the actual number of chunks
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split_sizes = []
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for v, spec in zip(values, chunk_specs, strict=True):
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# The original logic is "spec is _Replicate". This doesn't seem to be
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# correct. But we keep it for backward compatibility.
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if spec is _Replicate or isinstance(spec, _Replicate):
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split_sizes.append(num_chunks)
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elif isinstance(v, torch.Tensor):
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assert isinstance(spec, TensorChunkSpec)
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split_sizes.append(v.size(spec.split_dim))
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elif isinstance(v, BlockMask):
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assert isinstance(spec, TensorChunkSpec)
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assert spec.split_dim == 0, "BlockMask only supports split_dim=0"
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# BlockMask will broadcast if B is 1.
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if v.kv_num_blocks.size(0) == 1:
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split_sizes.append(num_chunks)
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else:
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split_sizes.append(v.kv_num_blocks.size(0))
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else:
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raise ValueError(
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f"Unsupported chunk spec: {spec} and value: {v} combination."
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)
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result_num_chunks = min(*split_sizes, num_chunks)
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flat_split_results: list[Any] = [[] for _ in range(result_num_chunks)]
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for v, spec in zip(values, chunk_specs, strict=True):
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v_splits: Sequence[Any] = []
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if spec is _Replicate or isinstance(spec, _Replicate):
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v_splits = [v] * result_num_chunks
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elif isinstance(v, torch.Tensor):
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v_splits = _split_tensor(v, spec, result_num_chunks)
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elif isinstance(v, BlockMask):
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v_splits = _split_block_mask(v, result_num_chunks)
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else:
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raise ValueError(
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f"Unsupported chunk spec: {spec} and value: {v} combination."
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)
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# pyrefly: ignore # no-matching-overload
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for _flat_split_result, _v_split in zip(
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flat_split_results, v_splits, strict=True
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):
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_flat_split_result.append(_v_split)
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return [
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tree_unflatten(_flat_split_result, tree_spec)
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for _flat_split_result in flat_split_results
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]
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def split_args_kwargs_into_chunks(
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args: tuple[Any, ...],
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kwargs: Optional[dict[str, Any]],
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chunks: int,
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args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
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kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
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) -> tuple[list[tuple], list[dict]]:
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"""
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Given a sequence of args and kwargs, split them into a number of chunks
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according to their respective chunking specs.
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Args:
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args: Tuple of args
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kwargs: Dict of kwargs
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chunks: Number of chunks to split the args and kwargs into
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args_chunk_spec: chunking specs for args, in same shape as args
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kwargs_chunk_spec: chunking specs for kwargs, in same shape as kwargs
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Returns:
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args_split: List of sharded args
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kwargs_split: List of sharded kwargs
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"""
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# Given `args` and `kwargs`, we want to yield a set of `chunks` args and kwargs such that
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# the constituent Tensor values have been sharded/replicated according to the `args_chunk_spec`
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# and `kwargs_chunk_spec` specifications. The steps are as follows:
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#
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# 1. Use pytree.tree_flatten to flatten each arg and its spec into nto a 1d array of values.
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# To use a running example: suppose our inputs look like
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#
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# args = ([A, [B, C]], D) args_spec = ([None, [None, TensorChunkSpec]], None)
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# (kwargs not shown but it's a similar process)
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#
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# Then for this step we would end up with
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#
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# args = ([A, B, C], D) args_spec = ([None, None, TensorChunkSpec], None)
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#
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# 2. Shard or replicate the arguments subject to the policy in the spec. Suppose chunks = 2
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#
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# args = ([[A, A], [B, B], [C_1, C_2]], [D, D])
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#
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# 3. Rotate the nesting order such that chunks are the outer dimension
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#
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# args_chunks = [
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# ([A, B, C_1], D),
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# ([A, B, C_2], D),
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# ]
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#
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# 4. Unflatten each chunk according to the spec
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#
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# args_chunks = [
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# ([A, [B, C_1]], D),
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# ([A, [B, C_2]], D),
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# ]
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# TODO: _debug_mask_minibatches
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# Handle the case where kwargs is None
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if kwargs is None:
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kwargs = {}
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# If user did not provide args_chunk_spec or kwargs_chunk_spec, we extend
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# their format and use default chunking along dim 0
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def default_spec(v):
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if isinstance(v, torch.Tensor | BlockMask):
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return TensorChunkSpec(DEFAULT_CHUNK_DIM)
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else:
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return _Replicate()
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if args_chunk_spec is None:
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args_chunk_spec = tree_map(default_spec, args)
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if kwargs_chunk_spec is None:
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kwargs_chunk_spec = tree_map(default_spec, kwargs)
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args_split_dict = _shard_dict_of_args(
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dict(enumerate(args)),
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dict(enumerate(args_chunk_spec)),
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chunks,
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)
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real_num_chunks = len(args_split_dict)
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kwargs_split = _shard_dict_of_args(
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kwargs,
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kwargs_chunk_spec,
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real_num_chunks,
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)
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if len(kwargs_split) < real_num_chunks:
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# In case kwargs are sharded into less chunks
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# e.g. when `args` has no tensor, just values
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real_num_chunks = len(kwargs_split)
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# Re-shard args
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args_split_dict = _shard_dict_of_args(
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dict(enumerate(args)),
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dict(enumerate(args_chunk_spec)),
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real_num_chunks,
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)
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if len(args_split_dict) != len(kwargs_split):
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raise RuntimeError(
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"args and kwargs are split into different number of chunks: "
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f"{len(args_split_dict)}, {len(kwargs_split)}"
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)
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args_split = [
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tuple(chunk_args[i] for i in range(len(chunk_args)))
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for chunk_args in args_split_dict
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]
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return args_split, kwargs_split
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def merge_chunks(
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chunks: list[Any],
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chunk_spec,
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):
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"""
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Given a list of chunks, merge them into a single value according to
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the chunk spec.
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Args:
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chunks: list of chunks
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chunk_spec: Chunking spec for the chunks
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Returns:
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value: Merged value
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"""
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# This is essentially the inverse of `split_args_kwargs_into_chunks`, so the
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# steps are similar to the steps in that function but in reverse. Given the
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# input values:
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#
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# chunks = [
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# ([A, [B, C_1]], D),
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# ([A, [B, C_2]], D),
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# ]
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# args_spec = ([None, [None, TensorChunkSpec]], None)
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#
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# 1. Flatten the chunks according to the chunk_spec
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#
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# chunks_flat = [
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# ([A, B, C_1], D),
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# ([A, B, C_2], D),
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# ]
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#
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# 2. Rotate the nesting order such that chunks are the inner dimension
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#
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# value_inner = ([A, B, [C_1, C_2]], D)
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#
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# 3. Concatenate sharded arguments
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#
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# value_combined = ([A, B, C], D)
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#
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# 4. Unflatten the combined args given the spec
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#
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# value = ([A, [B, C]], D)
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# Preliminary: flatten the chunk spec
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if chunk_spec is not None:
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spec_flattened, flatten_spec = tree_flatten(chunk_spec)
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else:
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# If chunk_spec is not provided, we will merge chunks along the default dimension (0), for all output fields
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# We obtain the output structure by flattening chunk 0 and generate the chunk_spec
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chunk0_flat, flatten_spec = tree_flatten(chunks[0])
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spec_flattened = [TensorChunkSpec(DEFAULT_CHUNK_DIM)] * len(chunk0_flat)
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# Stage 1: flatten chunks
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# chunks_flattened : [num chunks, num args]
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chunks_flattened = []
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for chunk in chunks:
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chunk_flattened, _ = tree_flatten(chunk)
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if len(chunk_flattened) != len(spec_flattened):
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raise ValueError(f"Chunk {chunk} did not match chunk spec {chunk_spec}")
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chunks_flattened.append(chunk_flattened)
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# Stage 2 and 3: Rotate nesting order s.t. chunks are inner dimension and
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# concatenate sharded operands
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# args_flattened : [num args]
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args_flattened = []
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for arg_idx, arg in enumerate(spec_flattened):
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if isinstance(arg, TensorChunkSpec):
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partial_values = [
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chunks_flattened[chunk_idx][arg_idx]
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for chunk_idx in range(len(chunks_flattened))
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]
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if _debug_mask_minibatches:
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# Infer size of individual chunks by running `tensor_split` again
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overall_shape = partial_values[0].shape
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for val in partial_values[1:]:
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assert val.shape == overall_shape
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meta_chunks = torch.tensor_split(
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torch.empty(*overall_shape, device="meta"),
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sections=len(partial_values),
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dim=arg.split_dim,
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)
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values_to_cat = []
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chunk_start_idx = 0
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assert len(partial_values) == len(meta_chunks)
|
|
for partial_value, meta_chunk in zip(partial_values, meta_chunks):
|
|
chunk_end_idx = chunk_start_idx + meta_chunk.size(arg.split_dim)
|
|
|
|
slice_indices = [slice(None, None, None)] * partial_value.ndim
|
|
slice_indices[arg.split_dim] = slice(chunk_start_idx, chunk_end_idx)
|
|
sliced = partial_value[slice_indices]
|
|
values_to_cat.append(sliced)
|
|
|
|
chunk_start_idx = chunk_end_idx
|
|
|
|
else:
|
|
values_to_cat = partial_values
|
|
|
|
args_flattened.append(torch.cat(values_to_cat, dim=arg.split_dim))
|
|
elif isinstance(arg, _CustomReducer):
|
|
reduced_val = arg.init_value
|
|
|
|
for chunk_idx in range(len(chunks_flattened)):
|
|
reduced_val = arg.reduce_fn(
|
|
reduced_val, chunks_flattened[chunk_idx][arg_idx]
|
|
)
|
|
|
|
args_flattened.append(reduced_val)
|
|
else:
|
|
value = chunks_flattened[0][arg_idx]
|
|
for chunk_idx in range(1, len(chunks_flattened)):
|
|
assert chunks_flattened[chunk_idx][arg_idx] == value
|
|
args_flattened.append(value)
|
|
|
|
# Stage 4: Unflatten combined args
|
|
return tree_unflatten(args_flattened, flatten_spec)
|